In this paper, we test and present an implementation of an interaction aware particle filter presented in [1]} along with a critical review and comparison of results. The filter is evaluated at each time step by a Markov Chain Monte Carlo (MCMC) sampling step [2]}. The targets to track in the test sequence are modeled in a single joint state, and likelihoods are computed via template matching approach. To deal with interactions a Markov Random Field (MRF) is established at each time step for near-by targets. Pairwise interaction terms are computed for these neighbourhoods which are used too resolve tracker ambiguity during the sampling stage for targets in close proximity. The following subsections explain the concept of MCMC methods, the used interaction potential approach, how the two concepts are integrated into the MCMC MRF Particle Filter along with the used motion model and likelihood measure. The performance of the proposed approach is verified by comparison with a set of independent particle filters each tracking a single target with importance sampling [3]}, using the same test set and setup.
